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Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management

Authors: Ashkan Safari; Mohammadreza Daneshvar; Amjad Anvari-Moghaddam;

Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management

Abstract

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.

Country
Denmark
Keywords

Technology, QH301-705.5, QC1-999, Smart Grids, /dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure; name=SDG 9 - Industry, Innovation, and Infrastructure, Machine Learning, power systems, /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy, smart grids, energy management systems, /dk/atira/pure/sustainabledevelopmentgoals/partnerships; name=SDG 17 - Partnerships for the Goals, Biology (General), Renewable Energy Sources (RES), QD1-999, T, Physics, Artificial Intelligence (AI), Power Systems, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, machine learning, renewable energy sources (RESs), Energy Management Systems, TA1-2040

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Average
Top 10%
Green
gold